SoroushVahidi/scidocs huggingface dataset:
Autonomous Self-Assembly in Swarm-Bots
(2006)

In this paper, we discuss the self-assembling capabilities of the swarm-bot, a distributed robotics concept that lies at the intersection between collective and self-reconfigurable robotics. A swarm-bot is comprised of autonomous mobile robots called s-bots. S-bots can either act independently or self-assemble into a swarm-bot by using their grippers. We report on experiments in which we study the process that leads a group of s-bots to self-assemble. In particular, we present results of experiments in which we vary the number of s-bots (up to 16 physical robots), their starting configurations, and the properties of the terrain on which self-assembly takes place. In view of the very successful experimental results, swarm-bot qualifies as the current state of the art in autonomous self-assembly
Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks
(2017)

Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed method is evaluated using four publicly available databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.
A Java API and Web Service Gateway for wireless M-Bus
(2013)

M-Bus Systems are used to support the remote data exchange with meter units through a network. They provide an easy extend able and cost effective method to connect many meter devices to one coherent network. Master applications operate as concentrated data collectors and are used to process measured values further. As modern approaches facilitate the possibility of a Smart Grid, M-Bus can be seen as the foundation of this technology. With the current focus on a more effective power grid, Smart Meters and Smart Grids are an important research topic. This bachelor thesis first gives an overview of the M-Bus standard and then presents a Java library and API to access M-Bus devices remotely in a standardized way through Web Services. Integration into common IT applications requires interoperable interfaces which also facilitate automated machine-to-machine communication. The oBIX (Open Building Information Exchange) standard provides such standardized objects and thus is used to create a Web Service gateway for the Java API.
AWEsome: An open-source test platform for airborne wind energy systems
(2017)

In this paper we present AWEsome (Airborne Wind Energy Standardized Open-source Model Environment), a test platform for airborne wind energy systems that consists of low-cost hardware and is entirely based on open-source software. It can hence be used without the need of large financial investments, in particular by research groups and startups to acquire first experiences in their flight operations, to test novel control strategies or technical designs, or for usage in public relations. Our system consists of a modified off-the-shelf model aircraft that is controlled by the pixhawk autopilot hardware and the ardupilot software for fixed wing aircraft. The aircraft is attached to the ground by a tether. We have implemented new flight modes for the autonomous tethered flight of the aircraft along periodic patterns. We present the principal functionality of our algorithms. We report on first successful tests of these modes in real flights. ∗Author names are in alphabetical order. a Universität Bonn, bechtle@physik.uni-bonn.de b Universität Bonn, thomasgehrmann@gmx.net c Humboldt-Universität zu Berlin, csieg@physik.hu-berlin.de d Daidalos Capital, zillmann@daidalos-capital.com awesome.physik.uni-bonn.de 1 ar X iv :1 70 4. 08 69 5v 1 [ cs .S Y ] 2 7 A pr 2 01 7 Section
Optimization of Belt-Type Electrostatic Separation of Triboaerodynamically Charged Granular Plastic Mixtures
(2013)

Electrostatic separation is frequently employed for the selective sorting of conducting and insulating materials for waste electric and electronic equipment. In a series of recent papers, the authors have presented several novel triboelectrostatic separation installations for the recycling of the main categories of plastics contained in information technology wastes. The aim of the present work is to optimize the design and the operation of such an installation, composed of a belt-type electrostatic separator associated to a propeller-type tribocharging device. The experimental design method is employed for modeling and optimizing the separation of a mixture of high-impact polystyrene and acrylonitrile butadiene styrene granules originating from shredded out-of-use computer cases. A distinct experiment is carried out on three synthetic mixtures of virgin plastic granules: 75% polyamide (PA)+ 25% polycarbonate (PC), 50% PA+ 50% PC, and 25% PA + 75% PC. The best results are obtained for the mixtures containing equal quantities of the two constituents.
Transformerless micro-inverter for grid-connected photovoltaic systems
(2017)

The leakage currents caused by high-frequency common-mode (CM) voltage have become a major concern in transformerless photovoltaic (PV) inverters. This paper addresses to a review on dc-ac converters applied to PV systems that can avoid the circulation of leakage currents. Looking for a lower cost and higher reliability solution, a 250 W PV transformerless micro-inverter prototype based on the bipolar full-bridge topology was built and tested. As it is confirmed by experimental results, this topology is able to maintain the CM voltage constant and prevent the circulation of CM currents through the circuit.
Efficient Program Analyses Using Deductive and Semantic Methodologies
(2017)

Program analysis is the process of gathering deeper insights about a source code and analysing them to resolve software problems of arbitrary complexity. The key challenge in program analysis is to keep it fast, precise and straightforward. This research focuses on three key objectives to achieve an efficient program analysis: (i) expressive data representation, (ii) optimised data structure and (iii) fast data processing mechanisms. State of the art technologies such as Resource Description Framework (RDF) as data representation format, triplestores as the storage &amp; processing layer, and datalog to represent program analysis rules are considered in our research. diagram(BDD) to be embedded in the triplestore. Additionally, an ontology is being designed to standardise the definitions of concepts and representation of the knowledge in the program analysis domain.
Neuroimaging: Decoding mental states from brain activity in humans
(2006)

Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.
Content-based multimedia information retrieval: State of the art and challenges
(2006)

Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100+ recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.
Machine learning classifiers and fMRI: A tutorial overview
(2009)

Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).
A Bridging Framework for Model Optimization and Deep Propagation
(2018)

Optimizing task-related mathematical model is one of the most fundamental methodologies in statistic and learning areas. However, generally designed schematic iterations may hard to investigate complex data distributions in real-world applications. Recently, training deep propagations (i.e., networks) has gained promising performance in some particular tasks. Unfortunately, existing networks are often built in heuristic manners, thus lack of principled interpretations and solid theoretical supports. In this work, we provide a new paradigm, named Propagation and Optimization based Deep Model (PODM), to bridge the gaps between these different mechanisms (i.e., model optimization and deep propagation). On the one hand, we utilize PODM as a deeply trained solver for model optimization. Different from these existing network based iterations, which often lack theoretical investigations, we provide strict convergence analysis for PODM in the challenging nonconvex and nonsmooth scenarios. On the other hand, by relaxing the model constraints and performing end-to-end training, we also develop a PODM based strategy to integrate domain knowledge (formulated as models) and real data distributions (learned by networks), resulting in a generic ensemble framework for challenging real-world applications. Extensive experiments verify our theoretical results and demonstrate the superiority of PODM against these state-of-the-art approaches.
Fast Alternating LS Algorithms for High Order CANDECOMP/PARAFAC Tensor Factorizations
(2013)

CANDECOMP/PARAFAC (CP) has found numerous applications in wide variety of areas such as in chemometrics, telecommunication, data mining, neuroscience, separated representations. For an order- N tensor, most CP algorithms can be computationally demanding due to computation of gradients which are related to products between tensor unfoldings and Khatri-Rao products of all factor matrices except one. These products have the largest workload in most CP algorithms. In this paper, we propose a fast method to deal with this issue. The method also reduces the extra memory requirements of CP algorithms. As a result, we can accelerate the standard alternating CP algorithms 20-30 times for order-5 and order-6 tensors, and even higher ratios can be obtained for higher order tensors (e.g., N ≥ 10). The proposed method is more efficient than the state-of-the-art ALS algorithm which operates two modes at a time (ALSo2) in the Eigenvector PLS toolbox, especially for tensors with order N ≥ 5 and high rank.
Multi-label learning: a review of the state of the art and ongoing research
(2014)

Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. The paper presents an up-todate overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed and ongoing research are presented.
Occlusion-capable multiview volumetric three-dimensional display.
(2007)

Volumetric 3D displays are frequently purported to lack the ability to reconstruct scenes with viewer-position-dependent effects such as occlusion. To counter these claims, a swept-screen 198-view horizontal-parallax-only 3D display is reported here that is capable of viewer-position-dependent effects. A digital projector illuminates a rotating vertical diffuser with a series of multiperspective 768 x 768 pixel renderings of a 3D scene. Evidence of near-far object occlusion is reported. The aggregate virtual screen surface for a stationary observer is described, as are guidelines to construct a full-parallax system and the theoretical ability of the present system to project imagery outside of the volume swept by the screen.
Ranking Attacks Based on Vulnerability Analysis
(2010)

Now that multiple-known attacks can affect one software product at the same time, it is necessary to rank and prioritize those attacks in order to establish a better defense. The purpose of this paper is to provide a set of security metrics to rank attacks based on vulnerability analysis. The vulnerability information is retrieved from a vulnerability management ontology, which integrates commonly used standards like CVE, CWE, CVSS, and CAPEC. Among the benefits of ranking attacks through the method proposed here are: a more effective mitigation or prevention of attack patterns against systems, a better foundation to test software products, and a better understanding of vulnerabilities and attacks.
Activity recognition from acceleration data based on discrete consine transform and SVM
(2009)

This paper developed a high-accuracy human activity recognition system based on single tri-axis accelerometer for use in a naturalistic environment. This system exploits the discrete cosine transform (DCT), the Principal Component Analysis (PCA) and Support Vector Machine (SVM) for classification human different activity. First, the effective features are extracted from accelerometer data using DCT. Next, feature dimension is reduced by PCA in DCT domain. After implementing the PCA, the most invariant and discriminating information for recognition is maintained. As a consequence, Multi-class Support Vector Machines is adopted to distinguish different human activities. Experiment results show that the proposed system achieves the best accuracy is 97.51%, which is better than other approaches.
Language Problems and ADHD Symptoms: How Specific Are the Links?
(2016)

Symptoms of inattention and hyperactivity frequently co-occur with language difficulties in both clinical and community samples. We explore the specificity and strength of these associations in a heterogeneous sample of 254 children aged 5 to 15 years identified by education and health professionals as having problems with attention, learning and/or memory. Parents/carers rated pragmatic and structural communication skills and behaviour, and children completed standardised assessments of reading, spelling, vocabulary, and phonological awareness. A single dimension of behavioural difficulties including both hyperactivity and inattention captured behaviour problems. This was strongly and negatively associated with pragmatic communication skills. There was less evidence for a relationship between behaviour and language structure: behaviour ratings were more weakly associated with the use of structural language in communication, and there were no links with direct measures of literacy. These behaviour problems and pragmatic communication difficulties co-occur in this sample, but impairments in the more formal use of language that impact on literacy and structural communication skills are tied less strongly to behavioural difficulties. One interpretation is that impairments in executive function give rise to both behavioural and social communication problems, and additional or alternative deficits in other cognitive abilities impact on the development of structural language skills.
Soft tissue cephalometric analysis: diagnosis and treatment planning of dentofacial deformity.
(1999)

This article will present a new soft tissue cephalometric analysis tool. This analysis may be used by the orthodontist and surgeon as an aid in diagnosis and treatment planning. The analysis is a radiographic instrument that was developed directly from the philosophy expressed in Arnett and Bergman "Facial keys to orthodontic diagnosis and treatment planning, Parts I and II" (Am J Orthop Dentofacial Orthod 1993; 103:299-312 and 395-411). The novelty of this approach, as with the "Facial Keys" articles, is an emphasis on soft tissue facial measurement.
A DTN-Based Sensor Data Gathering for Agricultural Applications
(2011)

This paper presents our field experience in data collection from remote sensors. By letting tractors, farmers, and sensors have short-range radio communication devices with delay-disruption tolerant networking (DTN), we can collect data from those sensors to our central database. Although, several implementations have been made with cellular phones or mesh networks in the past, DTN-based systems for such applications are still under explored. The main objective of this paper is to present our practical implementation and experiences in DTN-based data collection from remote sensors. The software, which we have developed for this research, has about 50 kbyte footprint, which is much smaller than any other DTN implementation. We carried out an experiment with 39 DTN nodes at the University of Tokyo assuming an agricultural scenario. They achieved 99.8% success rate for data gathering with moderate latency, showing sufficient usefulness in data granularity.
Detection of Unhealthy Region of plant leaves using Neural Network
(2016)

A leaf is an organ of vascular plant and is the principal lateral appendage of the stem. Each leaf has a set of features that differentiate it from the other leaves, such as margin and shape. This work proposes a comparison of supervised plant leaves classification using different approaches, based on different representations of these leaves, and the chosen algorithm. Beginning with the representation of leaves, we presented leaves by a fine-scale margin feature histogram, by a Centroid Contour Distance Curve shape signature, or by an interior texture feature histogram in 64 element vector for each one, after we tried different combination among these features to optimize results. We classified the obtained vectors. Then we evaluate the classification using cross validation. The obtained results are very interesting and show the importance of each feature. The classification of plant leaf images with biometric features. Traditionally, the trained taxonomic perform this process by following various tasks. The taxonomic usually classify the plants based on flowering and associative phenomenon. It was found that this process was time consuming and difficult. The biometric features of plants leaf like venation make this classification easy. Leaf biometric feature are analyzed using computer based method like morphological feature analysis and artificial neural network and Naive bayes based classifier. KNN classification model take input as the leaf venation morphological feature and classify them into four different species. The result of this classification based on leaf venation is achieved 96.53% accuracy in the training of the model for classification of leaves provide the 91% accuracy in testing to classify the leaf images. Keyword: Artificial neural network, K-NN Classification, Leaf venation pattern, Morphological Features
Earlier Identification of Children with Autism Spectrum Disorder: An Automatic Vocalisation-Based Approach
(2017)

Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed in or beyond toddlerhood. ASD is defined by repetitive and restricted behaviours, and deficits in social communication. The early speech-language development of individuals with ASD has been characterised as delayed. However, little is known about ASD-related characteristics of pre-linguistic vocalisations at the feature level. In this study, we examined pre-linguistic vocalisations of 10-month-old individuals later diagnosed with ASD and a matched control group of typically developing individuals (N = 20). We segmented 684 vocalisations from parent-child interaction recordings. All vocalisations were annotated and signal-analytically decomposed. We analysed ASD-related vocalisation specificities on the basis of a standardised set (eGeMAPS) of 88 acoustic features selected for clinical speech analysis applications. 54 features showed evidence for a differentiation between vocalisations of individuals later diagnosed with ASD and controls. In addition, we evaluated the feasibility of automated, vocalisation-based identification of individuals later diagnosed with ASD. We compared linear kernel support vector machines and a 1-layer bidirectional long short-term memory neural network. Both classification approaches achieved an accuracy of 75% for subject-wise identification in a subject-independent 3-fold cross-validation scheme. Our promising results may be an important contribution en-route to facilitate earlier identification of ASD.
High sigma measurement of random threshold voltage variation in 14nm Logic FinFET technology
(2015)

Random variation of threshold voltage (Vt) in MOSFETs plays a central role in determining the minimum operating voltage of products in a given process technology. Properly characterizing Vt variation requires a large volume of measurements of minimum size devices to understand the high sigma behavior. At the same time, a rapid measurement approach is required to keep the total measurement time practical. Here we describe a new test structure and measurement approach that enables practical characterization of Vt distributions to high sigma and its application to 14nm Logic FinFET technology. We show that both NMOS and PMOS single fin devices have very low random Vt variation of 19mV and 24mV respectively, normally distributed out to +/-5σ.
Medicinal and pharmaceutical uses of seaweed natural products: A review
(2004)

In the last three decades the discovery of metabolites with biological activities from macroalgae has increased significantly. However, despite the intense research effort by academic and corporate institutions, very few products with real potential have been identified or developed. Based on Silverplatter MEDLINE and Aquatic Biology, Aquaculture & Fisheries Resources databases, the literature was searched for natural products from marine macroalgae in the Rhodophyta, Phaeophyta and Chlorophyta with biological and pharmacological activity. Substances that currently receive most attention from pharmaceutical companies for use in drug development, or from researchers in the field of medicine-related research include: sulphated polysaccharides as antiviral substances, halogenated furanones from Delisea pulchra as antifouling compounds, and kahalalide F from a species of Bryopsis as a possible treatment of lung cancer, tumours and AIDS. Other substances such as macroalgal lectins, fucoidans, kainoids and aplysiatoxins are routinely used in biomedical research and a multitude of other substances have known biological activities. The potential pharmaceutical, medicinal and research applications of these compounds are discussed.